CN106384209A - Method and device for improving configuration of intelligent products based on operation data - Google Patents
Method and device for improving configuration of intelligent products based on operation data Download PDFInfo
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Abstract
The invention relates to a method and a device for improving the configuration of intelligent products based on operation data. The method comprises the following steps: defining a target problem and an improvement weight of intelligent product improvement according to the demand of intelligent products and the improvement possibility; finding function modules corresponding to the target problem through a quality function deployment QFD method; calculating and fusing the operation data corresponding to the function modules according to a preset method; abstractly calculating the fused operation data by use of a factor analysis method, and explaining and defining the data items of the abstracted operation data as different needs of users for different function modules; performing k-means clustering on the users according to the abstracted operation data to get the sizes of specific needs of different types of users for different function modules; and describing the degree of satisfaction of different types of users with the function modules of different intelligent products according to the sizes of specific needs, and putting forward the configuration improvement requirements of the function modules of different intelligent products. The utilization rate of the function modules in intelligent products can be improved.
Description
Technical field
The present invention relates to intelligent artifact design field, more particularly, to a kind of intelligent artifact configuration based on service data
Improved method and device.
Background technology
At present, when designing intelligent artifact, for meeting the real needs of all users, need interpolation in intelligent artifact many
Plant functional module product.However, different user colony has different demands, the several functions module in intelligent artifact is led to only have
It is well used on a small quantity, cause functional module utilization rate in intelligent artifact relatively low.
Content of the invention
For defect of the prior art, the present invention provides a kind of configuration improved method of the intelligent artifact based on service data
And device, to solve not taking into full account in prior art that user's real needs lead to functional module utilization rate in intelligent artifact low
Problem.
Embodiments provide a kind of configuration improved method of the intelligent artifact based on service data, including：
The improved target problem of intelligent artifact is defined according to intelligent artifact demand and improved possibility and improves weight；
Find the functional module corresponding with described target problem using QFD method QFD；
Calculated according to presetting method and merge the corresponding service data of each functional module；
Using factor-analysis approach abstract calculate merge after service data, and will be abstract after data each data item solution
Release and be defined as the different demands to difference in functionality module for the user；
K-means cluster is carried out to user according to the service data after abstract, to obtain dissimilar user to different work(
The real needs size of energy module；
The satisfaction degree of the functional module to different intelligent product for the dissimilar user is described according to real needs size, and
The improvement proposing the configuration of different intelligent product function module requires.
Alternatively, described find the functional module corresponding with described target problem using QFD method QFD
Step include：
Described intelligent artifact demand is converted into product technology demand, to realize described intelligent artifact demand and product technology
Feature mutually maps；
Mapped according to the structure title of described product technology feature and described intelligent artifact to obtain improved target
Functional module.
Alternatively, the described step according to the presetting method calculating fusion corresponding data of each functional module includes screening institute
The step stating service data：
First, select to be connected the service data of sensor transmissions with the physical unit of target problem corresponding function module；
Secondly, select the benefit as described service data for the data of product and data basis information described in intelligent artifact
Make up the number evidence；
Finally, the data item of physical unit transmission selecting corresponding other functions module is to described target problem corresponding function
The supplementary data of module.
Alternatively, described presetting method includes：
Judge whether described service data includes all information can describe the demand to functional module for the user；
If it is not, then combine description to need to require to add corresponding data item with specialty；
Described service data is merged with the data item added.
Alternatively, described combination description needs to require to utilize below equation in the step add corresponding data item with specialty
Calculate each data item：
Nna=f (data item Sn1, data item Sn2, data item Sn3 ... ... data item Sni).
Alternatively, the abstract service data calculating after merging of described utilization factor-analysis approach, and will be abstract after fortune
The step that the different demands to difference in functionality module for the user were explained and be defined as to each data item of row data includes：
Collect the data set S of n-th functional module user's request of descriptionnAnd Nn；
Reject the data item of temporal information described in data set；
Process described data set S using factor-analysis approachnAnd NnMiddle remaining data item and related data obtain n-th
Variables set Y after the factorial analysis of functional modulen={ abstract data item Yn1, abstract data item Yn2... abstract data item Yni}；
By described variables set Yn={ abstract data item Yn1, abstract data item Yn2... abstract data item YniBe construed to use
The demand at family or set of preferences Pn={ demand or preference Pn1, demand or preference Pn2... demand or preference Pni, by time data item
It is defined as the use time preference of user.
Alternatively, described factor-analysis approach includes：
By data set SnAnd NnIn remaining data item as the input in factorial analysis, carry out the factor using SPSS software
Analysis and factor rotation.
Alternatively, described k-means cluster is carried out to user according to the service data after abstract, to obtain dissimilar use
The step of the real needs size to difference in functionality module for the family includes：
By corresponding for n-th functional module data set Pn={ demand or preference Pn1, demand or preference Pn2... demand or inclined
Good PniIn each data item as data input during k-means cluster analysis；
Determine user type quantity and calculate user clustering analysis result, including each demand in x type of user or partially
Good PniAverage and each demand of all users or preference PniAverage；Wherein said user type quantity is by clustering point
Analysis result and the user's granularity of division needed for enterprise determine；
According to demand or preference satisfaction is established rules really, determine the satisfaction to certain demand or preference for the dissimilar user
Degree；
Described demand or preference satisfaction are established rules really, including：
When x type of user to certain demand of functional module or the mean value of preference is all user's mean values 80% with
Under, claim the type user low to certain demand of this functional module or preference is inconspicuous, except time preference；
When x type of user is to certain demand of functional module or 80%- that the mean value of preference is all user's mean values
120%, claim this group of subscribers certain of this functional module to be moderate in one's demands or no obvious preference, except time preference；
When x type of user, to certain demand of functional module or the mean value of preference is all user's mean values 120%
More than, claim this group of subscribers certain demand height to this functional module or preference substantially, except time preference.
Second aspect, the embodiment of the present invention additionally provides a kind of configuration of the intelligent artifact based on service data and improves device,
Including：
Target problem definition module, improved for intelligent artifact is defined according to intelligent artifact demand and improved possibility
Target problem and improvement weight；
Functional module searching modul, relative with described target problem for being found using QFD method QFD
The functional module answered；
Service data calculates Fusion Module, merges each functional module corresponding operation number for calculating according to presetting method
According to；
Service data abstract module, for the data after being merged using the abstract calculating of factor-analysis approach, and will be abstract
The each data item of service data afterwards is explained and is defined as the different demands to difference in functionality module for the user；
Service data cluster module, for carrying out k-means cluster according to the service data after abstract to user, to obtain
The real needs size to difference in functionality module for the dissimilar user；
Improve and require to propose module, for describing dissimilar user to different intelligent product according to real needs size
The satisfaction degree of functional module, and propose different intelligent product function module configuration improvement require.
Alternatively, functional module searching modul includes：
Need and Feature Mapping unit, for described intelligent artifact demand is converted into product technology demand, to realize
State intelligent artifact demand mutually to map with product technology feature；
Feature and structure title map unit, for the structure name according to described product technology feature and described intelligent artifact
Claim to be mapped to obtain improved objective function module；
And/or,
Service data calculates Fusion Module and includes service data screening unit, and described service data screening unit is used for executing
Following steps include：
First, select to be connected the service data of sensor transmissions with the physical unit of target problem corresponding function module；
Secondly, select the benefit as described service data for the data of product and data basis information described in intelligent artifact
Make up the number evidence；
Finally, the data item of physical unit transmission selecting corresponding other functions module is to described target problem corresponding function
The supplementary data of module；
And/or,
Service data abstract module includes：
Data set collector unit, for collecting the data set S of n-th functional module user's request of descriptionnAnd Nn；
Data item culling unit, for rejecting the data item of temporal information described in data set；
Variables set acquiring unit, for processing described data set S using factor-analysis approachnAnd NnMiddle remaining data item with
And related data obtains the variables set Y after the factorial analysis of n-th functional modulen={ abstract data item Yn1, abstract data item
Yn2... abstract data item Yni}；
Variables set Interpretation unit, for by described variables set Yn={ abstract data item Yn1, abstract data item Yn2... abstract
Data item YniIt is construed to demand or set of preferences P of usern={ demand or preference Pn1, demand or preference Pn2... demand or inclined
Good Pni, time data item is defined as the use time preference of user；
And/or,
Service data cluster module includes：
Data input acquiring unit, for by corresponding for n-th functional module data set Pn={ demand or preference Pn1, need
Ask or preference Pn2... demand or preference PniIn each data item as data input during k-means cluster analysis；
K-means cluster analysis unit, for determining user type quantity and calculating user clustering analysis result, including x
Each demand or preference P in individual type of userniAverage and each demand of all users or preference PniAverage；Wherein
Described user type quantity is determined with the user's granularity of division needed for enterprise by cluster analysis result；
Satisfaction degree determining unit, for according to demand or preference satisfaction is established rules really, determining dissimilar user couple
Certain demand or the satisfaction degree of preference；
Described demand or preference satisfaction are established rules really, including：
When x type of user to certain demand of functional module or the mean value of preference is all user's mean values 80% with
Under, claim the type user low to certain demand of this functional module or preference is inconspicuous, except time preference；
When x type of user is to certain demand of functional module or 80%- that the mean value of preference is all user's mean values
120%, claim this group of subscribers certain of this functional module to be moderate in one's demands or no obvious preference, except time preference；
When x type of user, to certain demand of functional module or the mean value of preference is all user's mean values 120%
More than, claim this group of subscribers certain demand height to this functional module or preference substantially, except time preference.
As shown from the above technical solution, the present invention defines intelligent artifact according to intelligent artifact demand and improved possibility and changes
The target problem entering and improvement weight；Find the work(corresponding with described target problem using QFD method QFD
Can module；Calculated according to presetting method and merge the corresponding service data of each functional module；Using the abstract meter of factor-analysis approach
Calculate merge after data, and will be abstract after each data item of service data explain and be defined as user to difference in functionality module
Different demands；K-means cluster is carried out to user according to the service data after abstract, to obtain dissimilar user to different work(
The real needs size of energy module；The functional module to different intelligent product for the dissimilar user is described according to real needs size
Satisfaction degree, and propose different intelligent product function module configuration improvement require.Compared with prior art, the present invention is permissible
According to the actual demand to functional module for the dissimilar user, improve the configuration of intelligent artifact functional module, thus improving intelligence
The utilization rate of functional module in product.
Brief description
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
Have technology description in required use accompanying drawing make one simple introduce it should be apparent that, drawings in the following description are these
Some bright embodiments, for those of ordinary skill in the art, on the premise of not paying creative work, can also root
Obtain other accompanying drawings according to these accompanying drawings.
Fig. 1 is that a kind of intelligent artifact configuration improved method flow process based on service data that one embodiment of the invention provides is shown
It is intended to；
Fig. 2 is that a kind of intelligent artifact configuration based on service data that another embodiment of the present invention provides improves device frame
Figure.
Specific embodiment
Purpose, technical scheme and advantage for making the embodiment of the present invention are clearer, below in conjunction with the embodiment of the present invention
In accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described it is clear that described embodiment is
The a part of embodiment of the present invention, rather than whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art
The every other embodiment being obtained under the premise of not making creative work, broadly falls into the scope of protection of the invention.
Embodiments provide a kind of configuration improved method of the intelligent artifact based on service data, as shown in figure 1, bag
Include：
S1, the improved target problem of intelligent artifact is defined according to intelligent artifact demand and improved possibility and improves power
Weight；
S2, find the functional module corresponding with described target problem using QFD method QFD；
S3, according to presetting method calculate merge the corresponding service data of each functional module；
S4, using factor-analysis approach abstract calculate merge after service data, and will be abstract after service data each
Data item is explained and is defined as the different demands to difference in functionality module for the user；
S5, k-means cluster is carried out to user according to the service data after abstract, to obtain dissimilar user to difference
The real needs size of functional module；
S6, describe dissimilar user according to real needs size journey is met to the functional module of different intelligent product
Spend, and propose the improvement of different intelligent product function module configuration and require.
Improve with reference to a kind of intelligent artifact configuration based on service data that embodiment and accompanying drawing provide to the present invention
The each step of method elaborates.
First, introduce S1, the improved target problem of intelligent artifact is defined with improved possibility according to intelligent artifact demand
And the step improving weight.
Above-mentioned intelligent artifact refers to the intelligent artifact of reality or the intelligence designing according to enterprise or consumer demand
Product model.This intelligent artifact includes multiple functional modules, is used for meeting enterprise or consumer demand.Need explanation
Be, above-mentioned enterprise or consumer-defined for client.
Improve weight and refer to that each target problem needs improved priority and importance degree.
The actual demand to intelligent artifact according to enterprise in the embodiment of the present invention, and in existing intelligent artifact or intelligence
The energy improved possibility of product model, defines the improved target problem of this intelligent artifact and improves weight.
Secondly, introduce S2, find the function mould corresponding with described target problem using QFD method QFD
Block.
Above-mentioned QFD method refers to, the Desire demand of consumer or enterprise is transformed into product or service
Current demand detailed system tool.
Above-mentioned functions module refers to the functional unit for completing all or part of function in target problem.
In the embodiment of the present invention, first with QFD method, described intelligent artifact demand is converted into product technology demand, with
Realize intelligent artifact demand mutually to map with product technology feature；Secondly according to described product technology feature and described intelligent artifact
Structure title mapped to obtain improved objective function module.In practical application, in intelligent artifact demand and product skill
Art feature also needs to when mutually mapping determine relative importance degree.When the structure title of product technology feature and intelligent artifact maps
It is also required to determine relative importance degree.
In the embodiment of the present invention, improved objective function module can be obtained by above-mentioned steps, including functional module 1,
Functional module 2 ..., functional module n, n is depending on the functional module that QFD obtains.
Again, introduce S3, the step merging the corresponding data of each functional module is calculated according to presetting method.
Above-mentioned presetting method includes：
Judge in step S2, whether service data includes all information can describe the demand to functional module for the user；
If it is not, then combine description to need to require to add corresponding data item with specialty；For example, n-th functional module is described
Side information data set Nn={ data item Nn1, data item Nn2 ... ... data item Nna }, the retouching of the size visual function module of a
Depending on stating situation.Wherein data item Nna=f (data item Sn1, data item Sn2, data item Sn3 ... ... data item Sni), function
Determination determined by the professional formula calculating data item Nna, obtain adding information data item collection.
Described service data is merged with the data item added.
Included according to the step that above-mentioned presetting method carries out calculating fusion in the embodiment of the present invention：
First, select to be connected the service data of sensor transmissions with the physical unit of target problem corresponding function module；
Secondly, select the benefit as described service data for the data of product and data basis information described in intelligent artifact
Make up the number evidence；
Finally, the data item of physical unit transmission selecting corresponding other functions module is to described target problem corresponding function
The supplementary data of module.
In the embodiment of the present invention, the item set of definition n-th functional module of description is service data collection n, data set Sn
={ data item Sn1, data item Sn2, data item Sn3 ... ... data item Sni }, the description situation of size visual function module of i and
Fixed.
4th, introduce S4, using factor-analysis approach abstract calculate merge after service data, and will be abstract after fortune
The step that each data item of row data is explained and is defined as the different demands to difference in functionality module for the user.
In practical application, step S4 includes：
Collect the data set S of n-th functional module user's request of descriptionnAnd Nn；Reject time letter described in data set
The data item of breath.
Process described data set S using factor-analysis approachnAnd NnMiddle remaining data item and related data obtain n-th
Variables set Y after the factorial analysis of functional modulen={ abstract data item Yn1, abstract data item Yn2... abstract data item Yni}；
Wherein, depending on the result by factorial analysis and rotation for the i.
By described variables set Yn={ abstract data item Yn1, abstract data item Yn2... abstract data item YniBe construed to use
The demand at family or set of preferences Pn={ demand or preference Pn1, demand or preference Pn2... demand or preference Pni, by time data item
It is defined as the use time preference of user.
It should be noted that above-mentioned factor-analysis approach refers to, by data set SnAnd NnIn remaining data item as the factor
Input in analysis, carries out factorial analysis and factor rotation using SPSS software.
5th, introduce S5, k-means cluster is carried out to user according to the service data after abstract, dissimilar to obtain
The step of the real needs size to difference in functionality module for the user.
In the present invention, by corresponding for n-th functional module data set Pn={ demand or preference Pn1, demand or preference
Pn2... demand or preference PniIn each data item as data input during k-means cluster analysis；Determine afterwards and use
Family number of types simultaneously calculates user clustering analysis result, including each demand or preference P in x type of userniAverage and
Each demand of all users or preference PniAverage；Wherein said user type quantity is needed for cluster analysis result and enterprise
User's granularity of division determine；Finally, according to demand or preference satisfaction is established rules really, determine dissimilar user to certain
Demand or the satisfaction degree of preference.
The demand or preference satisfaction are established rules really, including：
When x type of user to certain demand of functional module or the mean value of preference is all user's mean values 80% with
Under, claim the type user low to certain demand of this functional module or preference is inconspicuous, except time preference；
When x type of user is to certain demand of functional module or 80%- that the mean value of preference is all user's mean values
120%, claim this group of subscribers certain of this functional module to be moderate in one's demands or no obvious preference, except time preference；
When x type of user, to certain demand of functional module or the mean value of preference is all user's mean values 120%
More than, claim this group of subscribers certain demand height to this functional module or preference substantially, except time preference.
Finally, introduce S6, describe the functional module to different intelligent product for the dissimilar user according to real needs size
Satisfaction degree, and propose different intelligent product function module configuration improve require step.
Configure the superior of improved method for embodying a kind of intelligent artifact based on service data provided in an embodiment of the present invention
Property, as shown in Fig. 2 the embodiment of the present invention additionally provides a kind of configuration of the intelligent artifact based on service data and improves device, including：
Target problem definition module M1, improves for defining intelligent artifact according to intelligent artifact demand and improved possibility
Target problem and improve weight；
Functional module searching modul M2, for being found and described target problem phase using QFD method QFD
Corresponding functional module；
Service data calculates Fusion Module M3, merges the corresponding operation of each functional module for calculating according to presetting method
Data；
Service data abstract module M4, for the data after being merged using the abstract calculating of factor-analysis approach, and will take out
As after each data item of service data explain and be defined as the different demands to difference in functionality module for the user；
Service data cluster module M5, for carrying out k-means cluster according to the service data after abstract to user, to obtain
Take the real needs size to difference in functionality module for the dissimilar user；
Improve and require to propose module M6, for describing dissimilar user to different intelligent product according to real needs size
Functional module satisfaction degree, and propose different intelligent product function module configuration improvement require.
Alternatively, functional module searching modul includes：
Need and Feature Mapping unit, for described intelligent artifact demand is converted into product technology demand, to realize
State intelligent artifact demand mutually to map with product technology feature；
Feature and structure title map unit, for the structure name according to described product technology feature and described intelligent artifact
Claim to be mapped to obtain improved objective function module；
Alternatively, service data calculating Fusion Module includes service data screening unit, and described data screening unit is used for
Execution following steps include：
First, select to be connected the service data of sensor transmissions with the physical unit of target problem corresponding function module；
Secondly, select the benefit as described service data for the data of product and data basis information described in intelligent artifact
Make up the number evidence；
Finally, the data item of physical unit transmission selecting corresponding other functions module is to described target problem corresponding function
The supplementary data of module；
Alternatively, service data abstract module M4 includes：
Data set collector unit, for collecting the data set S of n-th functional module user's request of descriptionnAnd Nn；
Data item culling unit, for rejecting the data item of temporal information described in data set；
Variables set acquiring unit, for processing described data set S using factor-analysis approachnAnd NnMiddle remaining data item with
And related data obtains the variables set Y after the factorial analysis of n-th functional modulen={ abstract data item Yn1, abstract data item
Yn2... abstract data item Yni}；
Variables set Interpretation unit, for by described variables set Yn={ abstract data item Yn1, abstract data item Yn2... abstract
Data item YniIt is construed to demand or set of preferences P of usern={ demand or preference Pn1, demand or preference Pn2... demand or inclined
Good Pni, time data item is defined as the use time preference of user；
Alternatively, service data cluster module M5 includes：
Data input acquiring unit, for by corresponding for n-th functional module data set Pn={ demand or preference Pn1, need
Ask or preference Pn2... demand or preference PniIn each data item as data input during k-means cluster analysis；
K-means cluster analysis unit, for determining user type quantity and calculating user clustering analysis result, including x
Each demand or preference P in individual type of userniAverage and each demand of all users or preference PniAverage；Wherein
Described user type quantity is determined with the user's granularity of division needed for enterprise by cluster analysis result；
Satisfaction degree determining unit, for according to demand or preference satisfaction is established rules really, determining dissimilar user couple
Certain demand or the satisfaction degree of preference；
Described demand or preference satisfaction are established rules really, including：
When x type of user to certain demand of functional module or the mean value of preference is all user's mean values 80% with
Under, claim the type user low to certain demand of this functional module or preference is inconspicuous, except time preference；
When x type of user is to certain demand of functional module or 80%- that the mean value of preference is all user's mean values
120%, claim this group of subscribers certain of this functional module to be moderate in one's demands or no obvious preference, except time preference；
When x type of user, to certain demand of functional module or the mean value of preference is all user's mean values 120%
More than, claim this group of subscribers certain demand height to this functional module or preference substantially, except time preference.
The device that the present invention provides is based on method as discussed above and realizes, thus can solve same technical problem, and
Obtain identical technique effect, describe in detail and refer to embodiment of the method content, this is no longer going to repeat them
In sum, the present invention defines the improved target of intelligent artifact according to intelligent artifact demand and improved possibility and asks
Topic and improvement weight；Find the functional module corresponding with described target problem using QFD method QFD；According to
Presetting method calculates and merges the corresponding service data of each functional module；Using the abstract fortune calculating after merging of factor-analysis approach
Row data, and will be abstract after each data item of service data explain and be defined as user the difference of difference in functionality module is needed
Ask；K-means cluster is carried out to user according to the service data after abstract, to obtain dissimilar user to difference in functionality module
Real needs size；The satisfaction of the functional module to different intelligent product for the dissimilar user is described according to real needs size
Degree, and propose different intelligent product function module configuration improvement require.Compared with prior art, the present invention can be according to not
The actual demand to functional module for the same type user, improves the configuration of intelligent artifact functional module, thus improving in intelligent artifact
The utilization rate of functional module.
In the specification of the present invention, illustrate a large amount of details.Although it is understood that, embodiments of the invention can
To put into practice in the case of there is no these details.In some instances, known method, structure and skill are not been shown in detail
Art, so as not to obscure the understanding of this description.Similarly it will be appreciated that disclosing and help understand respectively to simplify the present invention
One or more of individual inventive aspect, in the description to the exemplary embodiment of the present invention above, each of the present invention is special
Levy and be sometimes grouped together in single embodiment, figure or descriptions thereof.However, should not be by the method solution of the disclosure
Release be intended to following in reflection：I.e. the present invention for required protection requires than the feature being expressly recited in each claim more
Many features.More precisely, as the following claims reflect, inventive aspect is less than single reality disclosed above
Apply all features of example.Therefore, it then follows claims of specific embodiment are thus expressly incorporated in this specific embodiment,
Wherein each claim itself is as the separate embodiments of the present invention.
Finally it should be noted that：Various embodiments above only in order to technical scheme to be described, is not intended to limit；To the greatest extent
Pipe has been described in detail to the present invention with reference to foregoing embodiments, it will be understood by those within the art that：Its according to
So the technical scheme described in foregoing embodiments can be modified, or wherein some or all of technical characteristic is entered
Row equivalent；And these modifications or replacement, do not make the essence of appropriate technical solution depart from various embodiments of the present invention technology
The scope of scheme, it all should be covered in the middle of the claim of the present invention and the scope of specification.
Claims (10)
1. a kind of intelligent artifact configuration improved method based on service data is it is characterised in that include：
The improved target problem of intelligent artifact is defined according to intelligent artifact demand and improved possibility and improves weight；
Find the functional module corresponding with described target problem using QFD method QFD；
Calculated according to presetting method and merge the corresponding service data of each functional module；
Using factor-analysis approach abstract calculate merge after service data, and will be abstract after each data item of data explain simultaneously
It is defined as the different demands to difference in functionality module for the user；
K-means cluster is carried out to user according to the service data after abstract, to obtain dissimilar user to difference in functionality mould
The real needs size of block；
Describe the satisfaction degree of the functional module to different intelligent product for the dissimilar user according to real needs size, and propose
The improvement of different intelligent product function module configuration requires.
2. intelligent artifact according to claim 1 configuration improved method is it is characterised in that described utilization quality function deployment
The step that method QFD finds the functional module corresponding with described target problem includes：
Described intelligent artifact demand is converted into product technology demand, to realize described intelligent artifact demand and product technology feature
Mutually map；
Mapped according to the structure title of described product technology feature and described intelligent artifact to obtain improved objective function
Module.
3. intelligent artifact according to claim 1 configuration improved method it is characterised in that described according to presetting method calculating
The step merging the corresponding service data of each functional module includes the step of screening described service data：
First, select to be connected the service data of sensor transmissions with the physical unit of target problem corresponding function module；
Secondly, select the supplementary number as described service data for the data of product and data basis information described in intelligent artifact
According to；
Finally, the data item of physical unit transmission selecting corresponding other functions module is to described target problem corresponding function module
Supplementary data.
4. intelligent artifact configuration improved method according to claim 3 is it is characterised in that described presetting method includes：
Judge whether described service data includes all information can describe the demand to functional module for the user；
If it is not, then combine description to need to require to add corresponding data item with specialty；
Described service data is merged with the data item added.
5. intelligent artifact configuration improved method according to claim 4 is it is characterised in that described combination description needs and special
Industry requires to calculate each data item using below equation in the step add corresponding data item：
Nna=f (data item Sn1, data item Sn2, data item Sn3 ... ... data item Sni).
6. intelligent artifact according to claim 1 configuration improved method is it is characterised in that described utilization factor-analysis approach
Abstract calculate merge after service data, and will be abstract after each data item of data explain and be defined as user to difference in functionality
The step of the different demands of module includes：
Collect the data set S of n-th functional module user's request of descriptionnAnd Nn；
Reject the data item of temporal information described in data set；
Process described data set S using factor-analysis approachnAnd NnMiddle remaining data item and related data obtain n-th function
Variables set Y after the factorial analysis of modulen={ abstract data item Yn1, abstract data item Yn2... abstract data item Yni}；
By described variables set Yn={ abstract data item Yn1, abstract data item Yn2... abstract data item YniIt is construed to the need of user
Ask or set of preferences Pn={ demand or preference Pn1, demand or preference Pn2... demand or preference Pni, time data item is defined as
The use time preference of user.
7. intelligent artifact according to claim 6 configuration improved method is it is characterised in that described factor-analysis approach bag
Include：
By data set SnAnd NnIn remaining data item as the input in factorial analysis, carry out factorial analysis using SPSS software
With factor rotation.
8. intelligent artifact according to claim 1 configure improved method it is characterised in that described according to the operation after abstract
Data carries out k-means cluster to user, to obtain the step of the real needs size to difference in functionality module for the dissimilar user
Rapid inclusion：
By corresponding for n-th functional module data set Pn={ demand or preference Pn1, demand or preference Pn2... demand or preference
PniIn each data item as data input during k-means cluster analysis；
Determine user type quantity and calculate user clustering analysis result, including each demand or preference P in x type of userni
Average and each demand of all users or preference PniAverage；Wherein said user type quantity is by cluster analysis result
Determine with the user's granularity of division needed for enterprise；
According to demand or preference satisfaction is established rules really, determine the satisfaction degree to certain demand or preference for the dissimilar user；
Described demand or preference satisfaction are established rules really, including：
When x type of user, to certain demand of functional module or the mean value of preference is all user's mean values less than 80%,
Claim the type user low to certain demand of this functional module or preference is inconspicuous, except time preference；
When x type of user is to certain demand of functional module or 80%- that the mean value of preference is all user's mean values
120%, claim this group of subscribers certain of this functional module to be moderate in one's demands or no obvious preference, except time preference；
When x type of user, to certain demand of functional module or the mean value of preference is all user's mean values more than 120%,
Claim this group of subscribers certain demand height to this functional module or preference substantially, except time preference.
9. a kind of intelligent artifact configuration based on service data improves device it is characterised in that including：
Target problem definition module, for defining the improved target of intelligent artifact according to intelligent artifact demand with improved possibility
Problem and improvement weight；
Functional module searching modul, corresponding with described target problem for being found using QFD method QFD
Functional module；
Service data calculates Fusion Module, merges the corresponding service data of each functional module for calculating according to presetting method；
Service data abstract module, for the service data after being merged using the abstract calculating of factor-analysis approach, and will be abstract
The each data item of service data afterwards is explained and is defined as the different demands to difference in functionality module for the user；
Service data cluster module, for carrying out k-means cluster according to the service data after abstract to user, to obtain difference
The real needs size to difference in functionality module for the type of user；
Improve and require to propose module, for describing the function to different intelligent product for the dissimilar user according to real needs size
The satisfaction degree of module, and propose different intelligent product function module configuration improvement require.
10. intelligent artifact according to claim 9 configuration improves device it is characterised in that functional module searching modul bag
Include：
Need and Feature Mapping unit, for described intelligent artifact demand is converted into product technology demand, to realize described intelligence
Can product demand mutually map with product technology feature；
Feature and structure title map unit, for entering according to the structure title of described product technology feature and described intelligent artifact
Row mapping is to obtain improved objective function module；
And/or,
Service data calculates Fusion Module and includes service data screening unit, and described service data screening unit is used for executing following
Step includes：
First, select to be connected the service data of sensor transmissions with the physical unit of target problem corresponding function module；
Secondly, select the supplementary number as described service data for the data of product and data basis information described in intelligent artifact
According to；
Finally, the data item of physical unit transmission selecting corresponding other functions module is to described target problem corresponding function module
Supplementary data；
And/or,
Service data abstract module includes：
Data set collector unit, for collecting the data set S of n-th functional module user's request of descriptionnAnd Nn；
Data item culling unit, for rejecting the data item of temporal information described in data set；
Variables set acquiring unit, for processing described data set S using factor-analysis approachnAnd NnMiddle remaining data item and phase
Close the variables set Y after the factorial analysis of n-th functional module of data acquisitionn={ abstract data item Yn1, abstract data item Yn2……
Abstract data item Yni}；
Variables set Interpretation unit, for by described variables set Yn={ abstract data item Yn1, abstract data item Yn2... abstract data
Item YniIt is construed to demand or set of preferences P of usern={ demand or preference Pn1, demand or preference Pn2... demand or preference
Pni, time data item is defined as the use time preference of user；
And/or,
Service data cluster module includes：
Data input acquiring unit, for by corresponding for n-th functional module data set Pn={ demand or preference Pn1, demand or
Preference Pn2... demand or preference PniIn each data item as data input during k-means cluster analysis；
K-means cluster analysis unit, for determining user type quantity and calculating user clustering analysis result, including x class
Each demand or preference P in type userniAverage and each demand of all users or preference PniAverage；Wherein said
User type quantity is determined with the user's granularity of division needed for enterprise by cluster analysis result；
Satisfaction degree determining unit, for according to demand or preference satisfaction is established rules really, determining dissimilar user to certain
Demand or the satisfaction degree of preference；
Described demand or preference satisfaction are established rules really, including：
When x type of user, to certain demand of functional module or the mean value of preference is all user's mean values less than 80%,
Claim the type user low to certain demand of this functional module or preference is inconspicuous, except time preference；
When x type of user is to certain demand of functional module or 80%- that the mean value of preference is all user's mean values
120%, claim this group of subscribers certain of this functional module to be moderate in one's demands or no obvious preference, except time preference；
When x type of user, to certain demand of functional module or the mean value of preference is all user's mean values more than 120%,
Claim this group of subscribers certain demand height to this functional module or preference substantially, except time preference.
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